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Prerequisite: MATH 120 with a minimum grade of "C-" or MATH 202 with a minimum grade of "C-"; and MATH 200 with a minimum grade of "C-" or MATH 403 with a minimum grade of "C-"; or consent of instructor This course provides an in-depth look at what regression is and how to effectively use it to produce and interpret statistical models. Topics include modeling assumptions, simple and multiple linear regression, covariance, correlation, confidence and prediction intervals, estimating regression parameters, tests of hypotheses about the regression coefficients, interpretations of regression coefficients especially in multiple linear models, various types of residuals, measures of influence, transformations of variables, weighted least squares, heteroscedasticity, and variable selection. The course includes hands-on active learning and applications of the concepts in specific contexts using statistical software. Offered fall semester.